library(sf)
## Linking to GEOS 3.8.1, GDAL 3.1.4, PROJ 6.3.1
library(readxl)
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(plyr)
## ------------------------------------------------------------------------------
## You have loaded plyr after dplyr - this is likely to cause problems.
## If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
## library(plyr); library(dplyr)
## ------------------------------------------------------------------------------
##
## Attaching package: 'plyr'
## The following objects are masked from 'package:dplyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize
library(ggplot2)
library(afrihealthsites)
library(ggpubr)
##
## Attaching package: 'ggpubr'
## The following object is masked from 'package:plyr':
##
## mutate
library(afriadmin)
##
## Attaching package: 'afriadmin'
## The following objects are masked from 'package:afrihealthsites':
##
## country2iso, iso2country
library(tmap)
# Install Malawi MFL
malawi_MFL = read_excel("~/malawi-health-facilities-1/MHFR_Facilities 1.xlsx")
head(malawi_MFL)
## # A tibble: 6 x 11
## CODE NAME `COMMON NAME` OWNERSHIP TYPE STATUS ZONE DISTRICT `DATE OPENED`
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 MC01… A + … A+A Private Clin… Funct… Cent… Mchinji Jan 1st 75
## 2 BT24… A-C … A.C Opticals Private Clin… Funct… Sout… Blantyre Jan 1st 75
## 3 MZ16… A-C … A-C Opticals Mission/… Clin… Non-f… Nort… Mzimba Jan 1st 75
## 4 BT24… Akwe… Akwezeke Pvt Private Clin… Funct… Sout… Blantyre Jan 1st 75
## 5 BT24… AB M… Abowa Private Clin… Funct… Sout… Blantyre Jan 1st 75
## 6 LL04… ABC … ABC Clinic Christia… Hosp… Funct… Cent… Lilongwe Jan 1st 75
## # … with 2 more variables: LATITUDE <chr>, LONGITUDE <chr>
# Convert to sf
## omit NA's
new_malawi_MFL = na.omit(malawi_MFL)
## check for NA
any(is.na(new_malawi_MFL))
## [1] FALSE
## transform geometry columns into numeric
sapply(new_malawi_MFL, class)
## CODE NAME COMMON NAME OWNERSHIP TYPE STATUS
## "character" "character" "character" "character" "character" "character"
## ZONE DISTRICT DATE OPENED LATITUDE LONGITUDE
## "character" "character" "character" "character" "character"
new_malawi_MFL = transform(new_malawi_MFL, LATITUDE = as.numeric(LATITUDE),
LONGITUDE = as.numeric(LONGITUDE))
## Warning in eval(substitute(list(...)), `_data`, parent.frame()): NAs introduced
## by coercion
any(is.na(new_malawi_MFL)) ## check for NA
## [1] TRUE
new_malawi_MFL = na.omit(new_malawi_MFL) ## and omit
## convert to sf object
malawi_facilities_MFL = st_as_sf(new_malawi_MFL, coords = c("LONGITUDE", "LATITUDE"), dim = "XY")
malawi_facilities_MFL = st_set_crs(malawi_facilities_MFL, 4326) ## set CRS, is WGS84 right?
malawi_facilities_MFL
## Simple feature collection with 1426 features and 9 fields
## geometry type: POINT
## dimension: XY
## bbox: xmin: 1 ymin: -542183 xmax: 70989710 ymax: -1
## geographic CRS: WGS 84
## First 10 features:
## CODE NAME
## 1 MC010002 A + A private clinic
## 2 BT240003 A-C Opticals
## 3 BT240005 Akwezeke PVT Clinic
## 4 BT240006 AB Medical Clinic
## 5 LL040007 ABC Comm. Hospital
## 6 LL040010 Achikondi Women Community Friendly Services Clinic
## 7 LL040011 Lilongwe Adventist Hospital
## 8 LL040012 Adventist Health Centre Area 15
## 9 LL040013 Africa Leaf Clinic Kanengo
## 10 ZA230014 MAIMED HEALTH CARE SERVICES
## COMMON.NAME OWNERSHIP
## 1 A+A Private
## 2 A.C Opticals Private
## 3 Akwezeke Pvt Private
## 4 Abowa Private
## 5 ABC Clinic Christian Health Association of Malawi (CHAM)
## 6 Achikondi Private
## 7 Adventist Health Centre Christian Health Association of Malawi (CHAM)
## 8 Adventist Christian Health Association of Malawi (CHAM)
## 9 Africa Leaf Clinic Kanengo Private
## 10 AHI Private
## TYPE STATUS ZONE DISTRICT DATE.OPENED
## 1 Clinic Functional Centrals West Zone Mchinji Jan 1st 75
## 2 Clinic Functional South East Zone Blantyre Jan 1st 75
## 3 Clinic Functional South East Zone Blantyre Jan 1st 75
## 4 Clinic Functional South East Zone Blantyre Jan 1st 75
## 5 Hospital Functional Centrals West Zone Lilongwe Jan 1st 75
## 6 Dispensary Functional Centrals West Zone Lilongwe Jan 1st 75
## 7 Hospital Functional Centrals West Zone Lilongwe Jan 1st 83
## 8 Health Centre Functional Centrals West Zone Lilongwe Jan 1st 75
## 9 Clinic Non-functional Centrals West Zone Lilongwe Jan 1st 75
## 10 Clinic Functional South West Zone Zomba Aug 1st 18
## geometry
## 1 POINT (33.88563 -13.79742)
## 2 POINT (35.03 -15.8)
## 3 POINT (35.09 -15.84)
## 4 POINT (35.09 -15.84)
## 5 POINT (33.74129 -13.96816)
## 6 POINT (33.7793 -13.95473)
## 7 POINT (33.7793 -13.95473)
## 8 POINT (33.7793 -13.95473)
## 9 POINT (33.80487 -13.8898)
## 10 POINT (35.3223 -15.38361)
Overview: 1. Malawi MFL - Facility locations managed by the Ministry of Health and Population Malawi. WHO guidance states that it should be updated at least every 2 years. Last update is unknown. - Locations in lat-long, CRS set to WGS84 - 1546 facilities reported, data includes name, ownership, type of facility and functional status.
# Number of each type
facility_types_MFL = as.data.frame(table(malawi_MFL$TYPE))
facility_types_MFL
## Var1 Freq
## 1 Central Hospital 4
## 2 Clinic 585
## 3 Dispensary 179
## 4 District Hospital 24
## 5 Health Centre 520
## 6 Health Post 138
## 7 Hospital 89
## 8 Private 6
## 9 Unclassified 1
## bar plot of no. of facility types
plot_facility_types_MFL = ggplot(facility_types_MFL, aes(x=Var1, y=Freq)) + geom_bar(stat = "identity")
plot_facility_types_MFL
# Number of each type of ownership
ownership_MFL = as.data.frame(table(malawi_MFL$OWNERSHIP))
ownership_MFL
## Var1 Freq
## 1 Aquaid Lifeline 1
## 2 Christian Health Association of Malawi (CHAM) 192
## 3 Government 695
## 4 Mission/Faith-based (other than CHAM) 62
## 5 Non-Government 69
## 6 Other 27
## 7 Parastatal 5
## 8 Private 495
## bar plot of ownership
plot_ownership_MFL = ggplot(ownership_MFL, aes(x=Var1, y=Freq)) + geom_bar(stat = "identity")
plot_ownership_MFL
Focuses on facilities run by government, faith-based organisations, NGO’s and local authorities. Covers 50 countries in sub-Saharan Africa. Sources of information include health sector reports, websites run by national or international organisations and personal communications
If MFL was available it was used. More than one datasource was often used to compile facility list
Private facilities are excluded, duplicates removed, name errors corrected and name variations were matched. Missing info was added with the use of other datasources.
Now hosted by the WHO Global Malaria Programme, last update February 2019
Malawi datasources includes MFL, https://data.humdata.org/dataset/malawi-health and http://www.cham.org.mw/uploads/7/3/0/8/73088105/cham_health_facilities_-_1_june_2016.pdf
At time of publishing, 639 facilities with 9 missing coordinates, not been updated since
Data includes facility name, type, ownership, source of location and reclassified facility types
# Malawi WHO data.frame
malawi_WHO <- afrihealthsites("malawi", datasource='who', plot=FALSE, returnclass='dataframe')
head(malawi_WHO)
## # A tibble: 6 x 10
## Country Admin1 `Facility name` `Facility type` Ownership Lat Long
## <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
## 1 Malawi Centr… 80 Block Clinic Clinic MoH -12.9 33.4
## 2 Malawi Centr… ABC Community … Clinic FBO -14.0 33.7
## 3 Malawi Centr… Adventist Heal… Health Centre FBO -14.0 33.8
## 4 Malawi Centr… Alinafe Commun… Community Hosp… FBO -13.4 34.2
## 5 Malawi Centr… Area 18 Health… Health Centre MoH -13.9 33.8
## 6 Malawi Centr… Area 25 Health… Health Centre MoH -13.9 33.8
## # … with 3 more variables: `LL source` <chr>, iso3c <chr>,
## # facility_type_9 <chr>
# No. of original facility types
facility_types_WHO = as.data.frame(table(malawi_WHO$`Facility type`))
facility_types_WHO
## Var1 Freq
## 1 Central Hospital 4
## 2 Clinic 22
## 3 Community Hospital 2
## 4 District Hospital 24
## 5 Health Centre 457
## 6 Health Post/Dispensary 87
## 7 Mission Hospital 27
## 8 Rural Hospital 25
## bar plot of original facility types
plot_facility_types_WHO = ggplot(facility_types_WHO, aes(x=Var1, y=Freq)) + geom_bar(stat = "identity")
plot_facility_types_WHO
# No. of reclassified facility types
RC_facility_types_WHO = as.data.frame(table(malawi_WHO$facility_type_9))
RC_facility_types_WHO
## Var1 Freq
## 1 Community Health Unit 2
## 2 Health Centre 457
## 3 Health Clinic 22
## 4 Health Post 87
## 5 Hospital 80
## bar plot of reclassified facility types
plot_RC_facility_types_WHO = ggplot(RC_facility_types_WHO, aes(x=Var1, y=Freq)) + geom_bar(stat = "identity")
plot_RC_facility_types_WHO
# Types of ownership
ownership_WHO = as.data.frame(table(malawi_WHO$Ownership))
ownership_WHO
## Var1 Freq
## 1 FBO 173
## 2 Local authority 5
## 3 MoH 467
## 4 NGO 3
## bar plot of ownership
plot_ownership_WHO = ggplot(ownership_WHO, aes(x=Var1, y=Freq)) + geom_bar(stat = "identity")
plot_ownership_WHO
Both data sources contain no information on services available, capacity or equipment. MFL does state whether facility is functional.
Classification of MFL facilities aligns more with the structure of the health care system in Malawi (community, primary, secondary, tertiary), it differentiates central hospitals from district and other hospitals. WHO has additional rural and mission hospitals, where do they fit in?
https://www.health.gov.mw/index.php/2016-01-06-19-58-23/national-aids states that at community level, health posts, dispensaries and maternity clinics offer services. Primary includes health centers and community hospitals, secondary consists of district and some CHAM hospitals, tertiary includes central hospitals.
Analysis:
# facility types
plot_facilities = ggarrange(plot_facility_types_MFL, plot_RC_facility_types_WHO,
ncol = 2,
labels = c("A", "B"))
plot_facilities
# ownership
plot_ownership = ggarrange(plot_ownership_MFL, plot_ownership_WHO,
ncol = 2,
labels = c("A", "B"))
plot_ownership
- Maps
# choose admin level
malawi_admin <- afriadmin("malawi",level=2, plot='sf')
# static WHO facility location map
map_static_WHO = afrihealthsites("malawi", datasource='who', plot='sf')
# combined plot of MFL and WHO facilities, static
plot(st_geometry(malawi_admin))
plot(st_geometry(map_static_WHO), add = TRUE)
plot(st_geometry(malawi_facilities_MFL), add = TRUE)
# tmap
tmap_mode("view")
## tmap mode set to interactive viewing
## admin map
tmap_admin = tm_shape(st_geometry(malawi_admin)) + tm_borders()
## MFL facility locations
tmap_facilities_MFL = tmap_admin + tm_shape(malawi_facilities_MFL) + tm_dots(col = "TYPE", palette = "viridis") + tm_layout(frame = FALSE, asp = 2, title = "MFL", title.position = c("left", "top"))
tmap_facilities_MFL
## Warning in sf::st_is_longlat(shp2): bounding box has potentially an invalid
## value range for longlat data
## WHO facility locations
tmap_facilities_WHO = tmap_admin + tm_shape(map_static_WHO) + tm_dots(col = "facility_type_9", palette = "viridis") + tm_layout(frame = FALSE, asp = 2, title = "WHO", title.position = c("left", "top"))
tmap_facilities_WHO
## combined
tmap_facilities_MFL_WHO = tmap_facilities_WHO + tm_shape(malawi_facilities_MFL) + tm_bubbles(col = "TYPE", palette = "YlOrRd", size = 0.05, alpha = 0.7) + tm_layout(frame = FALSE, asp = 2, title = "MFL & WHO", title.position = c("left", "top"))
tmap_facilities_MFL_WHO
## Warning in sf::st_is_longlat(shp2): bounding box has potentially an invalid
## value range for longlat data
## final
tmap_arrange(tmap_facilities_MFL, tmap_facilities_WHO, ncol = 2) # side by side
## Warning in sf::st_is_longlat(shp2): bounding box has potentially an invalid
## value range for longlat data
tmap_facilities_MFL_WHO = tmap_facilities_WHO + tm_shape(malawi_facilities_MFL) + tm_dots(col = "TYPE", palette = "YlOrRd", alpha = 0.7) + tm_layout(frame = FALSE, asp = 2, title = "MFL & WHO", title.position = c("left", "top"))
tmap_facilities_MFL_WHO # combined
## Warning in sf::st_is_longlat(shp2): bounding box has potentially an invalid
## value range for longlat data
Qs to address?:
# Qs 1 - how many intersect?
## convert malawi_WHO to sf object
class(malawi_WHO)
## [1] "tbl_df" "tbl" "data.frame"
any(is.na(malawi_WHO))
## [1] TRUE
new_malawi_WHO = na.omit(malawi_WHO) ## omit NA
sf_malawi_WHO = st_as_sf(new_malawi_WHO, coords = c("Long", "Lat"), dim = "XY")
sf_malawi_WHO = st_set_crs(sf_malawi_WHO, 4326)
## st_intersection
intersect_WHO_MFL = st_intersection(x=sf_malawi_WHO, y=malawi_facilities_MFL)
## although coordinates are longitude/latitude, st_intersection assumes that they are planar
## Warning: attribute variables are assumed to be spatially constant throughout all
## geometries
intersect_WHO_MFL ## only 2 intersect directly, so are same up to 5 decimal places?
## Simple feature collection with 2 features and 17 fields
## geometry type: POINT
## dimension: XY
## bbox: xmin: 33.29456 ymin: -11.53894 xmax: 33.41925 ymax: -11.45836
## geographic CRS: WGS 84
## # A tibble: 2 x 18
## Country Admin1 Facility.name Facility.type Ownership LL.source iso3c
## * <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 Malawi North… Euthini Heal… Health Centre MoH GPS MWI
## 2 Malawi North… Madede Healt… Health Centre MoH GPS MWI
## # … with 11 more variables: facility_type_9 <chr>, CODE <chr>, NAME <chr>,
## # COMMON.NAME <chr>, OWNERSHIP <chr>, TYPE <chr>, STATUS <chr>, ZONE <chr>,
## # DISTRICT <chr>, DATE.OPENED <chr>, geometry <POINT [°]>
# Qs 2 - Do they share same attributes
## ownership is same, 1 name is same, Euthini registered as a hospital in MFL but as a health centre in WHO
# Qs 3 - how many/which are within 50m of another facility?